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# Story 2.2: Summary Generation Pipeline
## Status
Done
## Story
**As a** user
**I want** an end-to-end pipeline that seamlessly processes YouTube URLs into high-quality summaries
**so that** I can get from video link to summary in a single, streamlined workflow
## Acceptance Criteria
1. Integrated pipeline connects URL validation → transcript extraction → AI summarization
2. Pipeline handles the complete workflow asynchronously with progress tracking
3. System provides intelligent summary optimization based on transcript characteristics
4. Generated summaries include enhanced metadata (video info, processing stats, quality scores)
5. Pipeline includes quality validation and automatic retry for failed summaries
6. Users can monitor pipeline progress and receive completion notifications
## Tasks / Subtasks
- [ ] **Task 1: Pipeline Orchestration Service** (AC: 1, 2)
- [ ] Create `SummaryPipeline` orchestrator in `backend/services/summary_pipeline.py`
- [ ] Implement workflow coordination between video, transcript, and AI services
- [ ] Add pipeline state management with persistent job tracking
- [ ] Create rollback and cleanup mechanisms for failed pipelines
- [ ] **Task 2: Enhanced Video Metadata Integration** (AC: 4)
- [ ] Integrate YouTube Data API for rich video metadata extraction
- [ ] Add video categorization and content type detection
- [ ] Implement thumbnail and channel information capture
- [ ] Create metadata-driven summary customization logic
- [ ] **Task 3: Intelligent Summary Optimization** (AC: 3, 5)
- [ ] Implement transcript analysis for content type detection (educational, entertainment, technical)
- [ ] Add automatic summary length optimization based on content complexity
- [ ] Create quality scoring algorithm for generated summaries
- [ ] Implement summary enhancement for poor-quality results
- [ ] **Task 4: Progress Tracking and Notifications** (AC: 2, 6)
- [ ] Create comprehensive pipeline progress tracking system
- [ ] Implement WebSocket notifications for real-time updates
- [ ] Add email notifications for completed summaries (optional)
- [ ] Create detailed logging and audit trail for each pipeline run
- [ ] **Task 5: Quality Assurance and Validation** (AC: 5)
- [ ] Implement summary quality validation checks
- [ ] Add automatic retry logic for failed or low-quality summaries
- [ ] Create fallback strategies for different types of failures
- [ ] Implement summary improvement suggestions and regeneration
- [ ] **Task 6: API Integration and Frontend** (AC: 1, 2, 6)
- [ ] Create `/api/process` endpoint for end-to-end pipeline processing
- [ ] Update frontend to use integrated pipeline instead of separate services
- [ ] Add pipeline status dashboard for monitoring active and completed jobs
- [ ] Implement pipeline cancellation and cleanup functionality
- [ ] **Task 7: Performance and Reliability** (AC: 2, 5)
- [ ] Add comprehensive error handling and recovery mechanisms
- [ ] Implement pipeline timeout and resource management
- [ ] Create performance monitoring and optimization tracking
- [ ] Add pipeline analytics and usage statistics
## Dev Notes
### Architecture Context
This story creates the core user-facing workflow that demonstrates the full value of the YouTube Summarizer. The pipeline must be reliable, fast, and provide clear feedback while handling edge cases gracefully.
### Pipeline Orchestration Architecture
[Source: docs/architecture.md#pipeline-architecture]
```python
# backend/services/summary_pipeline.py
import asyncio
import uuid
from datetime import datetime
from enum import Enum
from typing import Dict, Optional, List, Any
from dataclasses import dataclass, asdict
from ..services.video_service import VideoService
from ..services.transcript_service import TranscriptService
from ..services.anthropic_summarizer import AnthropicSummarizer
from ..services.cache_manager import CacheManager
from ..core.exceptions import PipelineError
class PipelineStage(Enum):
INITIALIZED = "initialized"
VALIDATING_URL = "validating_url"
EXTRACTING_METADATA = "extracting_metadata"
EXTRACTING_TRANSCRIPT = "extracting_transcript"
ANALYZING_CONTENT = "analyzing_content"
GENERATING_SUMMARY = "generating_summary"
VALIDATING_QUALITY = "validating_quality"
COMPLETED = "completed"
FAILED = "failed"
CANCELLED = "cancelled"
@dataclass
class PipelineConfig:
summary_length: str = "standard"
include_timestamps: bool = False
focus_areas: Optional[List[str]] = None
quality_threshold: float = 0.7
max_retries: int = 2
enable_notifications: bool = True
@dataclass
class PipelineProgress:
stage: PipelineStage
percentage: float
message: str
estimated_time_remaining: Optional[float] = None
current_step_details: Optional[Dict[str, Any]] = None
@dataclass
class PipelineResult:
job_id: str
video_url: str
video_id: str
status: PipelineStage
# Video metadata
video_metadata: Optional[Dict[str, Any]] = None
# Processing results
transcript: Optional[str] = None
summary: Optional[str] = None
key_points: Optional[List[str]] = None
main_themes: Optional[List[str]] = None
actionable_insights: Optional[List[str]] = None
# Quality and metadata
confidence_score: Optional[float] = None
quality_score: Optional[float] = None
processing_metadata: Optional[Dict[str, Any]] = None
cost_data: Optional[Dict[str, Any]] = None
# Timeline
started_at: Optional[datetime] = None
completed_at: Optional[datetime] = None
processing_time_seconds: Optional[float] = None
# Error information
error: Optional[Dict[str, Any]] = None
retry_count: int = 0
class SummaryPipeline:
"""Orchestrates the complete YouTube summarization workflow"""
def __init__(
self,
video_service: VideoService,
transcript_service: TranscriptService,
ai_service: AnthropicSummarizer,
cache_manager: CacheManager
):
self.video_service = video_service
self.transcript_service = transcript_service
self.ai_service = ai_service
self.cache_manager = cache_manager
# Active jobs tracking
self.active_jobs: Dict[str, PipelineResult] = {}
self.progress_callbacks: Dict[str, List[callable]] = {}
async def process_video(
self,
video_url: str,
config: PipelineConfig = None,
progress_callback: callable = None
) -> str:
"""Start video processing pipeline and return job ID"""
if config is None:
config = PipelineConfig()
job_id = str(uuid.uuid4())
# Initialize pipeline result
result = PipelineResult(
job_id=job_id,
video_url=video_url,
video_id="", # Will be populated during validation
status=PipelineStage.INITIALIZED,
started_at=datetime.utcnow(),
retry_count=0
)
self.active_jobs[job_id] = result
if progress_callback:
self.progress_callbacks[job_id] = [progress_callback]
# Start processing in background
asyncio.create_task(self._execute_pipeline(job_id, config))
return job_id
async def _execute_pipeline(self, job_id: str, config: PipelineConfig):
"""Execute the complete processing pipeline"""
result = self.active_jobs[job_id]
try:
# Stage 1: URL Validation
await self._update_progress(job_id, PipelineStage.VALIDATING_URL, 5, "Validating YouTube URL...")
video_id = await self.video_service.extract_video_id(result.video_url)
result.video_id = video_id
# Stage 2: Extract Video Metadata
await self._update_progress(job_id, PipelineStage.EXTRACTING_METADATA, 15, "Extracting video information...")
metadata = await self._extract_enhanced_metadata(video_id)
result.video_metadata = metadata
# Stage 3: Extract Transcript
await self._update_progress(job_id, PipelineStage.EXTRACTING_TRANSCRIPT, 35, "Extracting transcript...")
transcript_result = await self.transcript_service.extract_transcript(video_id)
result.transcript = transcript_result.transcript
# Stage 4: Analyze Content for Optimization
await self._update_progress(job_id, PipelineStage.ANALYZING_CONTENT, 50, "Analyzing content characteristics...")
content_analysis = await self._analyze_content_characteristics(result.transcript, metadata)
optimized_config = self._optimize_config_for_content(config, content_analysis)
# Stage 5: Generate Summary
await self._update_progress(job_id, PipelineStage.GENERATING_SUMMARY, 75, "Generating AI summary...")
summary_result = await self._generate_optimized_summary(result.transcript, optimized_config, content_analysis)
# Populate summary results
result.summary = summary_result.summary
result.key_points = summary_result.key_points
result.main_themes = summary_result.main_themes
result.actionable_insights = summary_result.actionable_insights
result.confidence_score = summary_result.confidence_score
result.processing_metadata = summary_result.processing_metadata
result.cost_data = summary_result.cost_data
# Stage 6: Quality Validation
await self._update_progress(job_id, PipelineStage.VALIDATING_QUALITY, 90, "Validating summary quality...")
quality_score = await self._validate_summary_quality(result, content_analysis)
result.quality_score = quality_score
# Check if quality meets threshold
if quality_score < config.quality_threshold and result.retry_count < config.max_retries:
await self._retry_with_improvements(job_id, config, "Low quality score")
return
# Stage 7: Complete
result.completed_at = datetime.utcnow()
result.processing_time_seconds = (result.completed_at - result.started_at).total_seconds()
result.status = PipelineStage.COMPLETED
await self._update_progress(job_id, PipelineStage.COMPLETED, 100, "Summary completed successfully!")
# Cache the result
await self.cache_manager.cache_pipeline_result(job_id, result)
# Send completion notification
if config.enable_notifications:
await self._send_completion_notification(result)
except Exception as e:
await self._handle_pipeline_error(job_id, e, config)
async def _extract_enhanced_metadata(self, video_id: str) -> Dict[str, Any]:
"""Extract rich video metadata using YouTube Data API"""
# This would integrate with YouTube Data API v3
# For now, implementing basic structure
try:
# Simulate YouTube Data API call
metadata = {
"title": f"Video {video_id} Title", # Would come from API
"description": "Video description...",
"channel_name": "Channel Name",
"published_at": datetime.utcnow().isoformat(),
"duration": "PT10M30S", # ISO 8601 duration
"view_count": 1000,
"like_count": 50,
"category": "Education",
"tags": ["python", "tutorial", "coding"],
"thumbnail_url": f"https://img.youtube.com/vi/{video_id}/maxresdefault.jpg",
"language": "en",
"default_language": "en"
}
return metadata
except Exception as e:
# Return basic metadata if enhanced extraction fails
return {
"video_id": video_id,
"title": f"Video {video_id}",
"error": f"Enhanced metadata extraction failed: {str(e)}"
}
async def _analyze_content_characteristics(self, transcript: str, metadata: Dict[str, Any]) -> Dict[str, Any]:
"""Analyze transcript and metadata to determine optimal processing strategy"""
analysis = {
"transcript_length": len(transcript),
"word_count": len(transcript.split()),
"estimated_reading_time": len(transcript.split()) / 250, # Words per minute
"complexity_score": 0.5, # Would implement actual complexity analysis
"content_type": "general",
"language": metadata.get("language", "en"),
"technical_indicators": [],
"educational_indicators": [],
"entertainment_indicators": []
}
# Basic content type detection
transcript_lower = transcript.lower()
# Technical content indicators
technical_terms = ["algorithm", "function", "variable", "database", "api", "code", "programming"]
technical_count = sum(1 for term in technical_terms if term in transcript_lower)
if technical_count >= 3:
analysis["content_type"] = "technical"
analysis["technical_indicators"] = [term for term in technical_terms if term in transcript_lower]
# Educational content indicators
educational_terms = ["learn", "tutorial", "explain", "understand", "concept", "example", "lesson"]
educational_count = sum(1 for term in educational_terms if term in transcript_lower)
if educational_count >= 3:
analysis["content_type"] = "educational"
analysis["educational_indicators"] = [term for term in educational_terms if term in transcript_lower]
# Entertainment content indicators
entertainment_terms = ["funny", "story", "experience", "adventure", "review", "reaction"]
entertainment_count = sum(1 for term in entertainment_terms if term in transcript_lower)
if entertainment_count >= 2:
analysis["content_type"] = "entertainment"
analysis["entertainment_indicators"] = [term for term in entertainment_terms if term in transcript_lower]
# Complexity scoring based on sentence length and vocabulary
sentences = transcript.split('.')
avg_sentence_length = sum(len(s.split()) for s in sentences) / len(sentences) if sentences else 0
if avg_sentence_length > 20:
analysis["complexity_score"] = min(1.0, analysis["complexity_score"] + 0.3)
elif avg_sentence_length < 10:
analysis["complexity_score"] = max(0.1, analysis["complexity_score"] - 0.2)
return analysis
def _optimize_config_for_content(self, base_config: PipelineConfig, analysis: Dict[str, Any]) -> PipelineConfig:
"""Optimize processing configuration based on content analysis"""
optimized_config = PipelineConfig(**asdict(base_config))
# Adjust summary length based on content
if analysis["word_count"] > 5000 and optimized_config.summary_length == "standard":
optimized_config.summary_length = "detailed"
elif analysis["word_count"] < 500 and optimized_config.summary_length == "standard":
optimized_config.summary_length = "brief"
# Add focus areas based on content type
if not optimized_config.focus_areas:
optimized_config.focus_areas = []
content_type = analysis.get("content_type", "general")
if content_type == "technical":
optimized_config.focus_areas.extend(["technical concepts", "implementation details"])
elif content_type == "educational":
optimized_config.focus_areas.extend(["learning objectives", "key concepts", "practical applications"])
elif content_type == "entertainment":
optimized_config.focus_areas.extend(["main highlights", "key moments", "overall message"])
# Adjust quality threshold based on complexity
if analysis["complexity_score"] > 0.7:
optimized_config.quality_threshold = max(0.6, optimized_config.quality_threshold - 0.1)
return optimized_config
async def _generate_optimized_summary(
self,
transcript: str,
config: PipelineConfig,
analysis: Dict[str, Any]
) -> Any: # Returns SummaryResult
"""Generate summary with content-aware optimizations"""
from ..services.ai_service import SummaryRequest, SummaryLength
# Map config to AI service parameters
length_mapping = {
"brief": SummaryLength.BRIEF,
"standard": SummaryLength.STANDARD,
"detailed": SummaryLength.DETAILED
}
summary_request = SummaryRequest(
transcript=transcript,
length=length_mapping[config.summary_length],
focus_areas=config.focus_areas,
language=analysis.get("language", "en"),
include_timestamps=config.include_timestamps
)
# Add content-specific prompt enhancements
if analysis["content_type"] == "technical":
summary_request.focus_areas.append("explain technical concepts clearly")
elif analysis["content_type"] == "educational":
summary_request.focus_areas.append("highlight learning outcomes")
return await self.ai_service.generate_summary(summary_request)
async def _validate_summary_quality(self, result: PipelineResult, analysis: Dict[str, Any]) -> float:
"""Validate and score summary quality"""
quality_score = 0.0
# Check summary length appropriateness
summary_word_count = len(result.summary.split()) if result.summary else 0
transcript_word_count = analysis["word_count"]
# Good summary should be 5-15% of original length
compression_ratio = summary_word_count / transcript_word_count if transcript_word_count > 0 else 0
if 0.05 <= compression_ratio <= 0.15:
quality_score += 0.3
elif 0.03 <= compression_ratio <= 0.20:
quality_score += 0.2
# Check key points availability and quality
if result.key_points and len(result.key_points) >= 3:
quality_score += 0.2
# Check main themes availability
if result.main_themes and len(result.main_themes) >= 2:
quality_score += 0.15
# Check actionable insights
if result.actionable_insights and len(result.actionable_insights) >= 1:
quality_score += 0.15
# Use AI confidence score
if result.confidence_score and result.confidence_score > 0.8:
quality_score += 0.2
elif result.confidence_score and result.confidence_score > 0.6:
quality_score += 0.1
return min(1.0, quality_score)
async def _retry_with_improvements(self, job_id: str, config: PipelineConfig, reason: str):
"""Retry pipeline with improved configuration"""
result = self.active_jobs[job_id]
result.retry_count += 1
await self._update_progress(
job_id,
PipelineStage.ANALYZING_CONTENT,
40,
f"Retrying with improvements (attempt {result.retry_count + 1}/{config.max_retries + 1})"
)
# Improve configuration for retry
improved_config = PipelineConfig(**asdict(config))
improved_config.summary_length = "detailed" # Try more detailed summary
improved_config.quality_threshold = max(0.5, config.quality_threshold - 0.1) # Lower threshold slightly
# Continue pipeline with improved config
await self._execute_pipeline(job_id, improved_config)
async def _handle_pipeline_error(self, job_id: str, error: Exception, config: PipelineConfig):
"""Handle pipeline errors with retry logic"""
result = self.active_jobs[job_id]
result.status = PipelineStage.FAILED
result.error = {
"message": str(error),
"type": type(error).__name__,
"stage": result.status.value,
"retry_count": result.retry_count
}
# Attempt retry if within limits
if result.retry_count < config.max_retries:
await asyncio.sleep(2 ** result.retry_count) # Exponential backoff
await self._retry_with_improvements(job_id, config, f"Error: {str(error)}")
else:
result.completed_at = datetime.utcnow()
await self._update_progress(job_id, PipelineStage.FAILED, 0, f"Failed after {result.retry_count + 1} attempts")
async def _update_progress(
self,
job_id: str,
stage: PipelineStage,
percentage: float,
message: str,
details: Dict[str, Any] = None
):
"""Update pipeline progress and notify callbacks"""
result = self.active_jobs.get(job_id)
if result:
result.status = stage
progress = PipelineProgress(
stage=stage,
percentage=percentage,
message=message,
current_step_details=details
)
# Notify all registered callbacks
callbacks = self.progress_callbacks.get(job_id, [])
for callback in callbacks:
try:
await callback(job_id, progress)
except Exception as e:
print(f"Progress callback error: {e}")
async def get_pipeline_result(self, job_id: str) -> Optional[PipelineResult]:
"""Get pipeline result by job ID"""
# Check active jobs first
if job_id in self.active_jobs:
return self.active_jobs[job_id]
# Check cache for completed jobs
cached_result = await self.cache_manager.get_cached_pipeline_result(job_id)
return cached_result
async def cancel_pipeline(self, job_id: str) -> bool:
"""Cancel running pipeline"""
if job_id in self.active_jobs:
result = self.active_jobs[job_id]
result.status = PipelineStage.CANCELLED
result.completed_at = datetime.utcnow()
await self._update_progress(job_id, PipelineStage.CANCELLED, 0, "Pipeline cancelled by user")
return True
return False
async def _send_completion_notification(self, result: PipelineResult):
"""Send completion notification (email, webhook, etc.)"""
# This would integrate with notification service
notification_data = {
"job_id": result.job_id,
"video_title": result.video_metadata.get("title", "Unknown") if result.video_metadata else "Unknown",
"status": result.status.value,
"processing_time": result.processing_time_seconds,
"summary_preview": result.summary[:100] + "..." if result.summary else None
}
# Log completion for now (would send actual notifications)
print(f"Pipeline completed: {notification_data}")
```
### API Integration
[Source: docs/architecture.md#api-specification]
```python
# backend/api/pipeline.py
from fastapi import APIRouter, HTTPException, BackgroundTasks, Depends
from pydantic import BaseModel, Field, HttpUrl
from typing import Optional, List, Dict, Any
from ..services.summary_pipeline import SummaryPipeline, PipelineConfig, PipelineStage
from ..core.websocket_manager import WebSocketManager
router = APIRouter(prefix="/api", tags=["pipeline"])
class ProcessVideoRequest(BaseModel):
video_url: HttpUrl = Field(..., description="YouTube video URL to process")
summary_length: str = Field("standard", description="Summary length preference")
focus_areas: Optional[List[str]] = Field(None, description="Areas to focus on in summary")
include_timestamps: bool = Field(False, description="Include timestamps in summary")
enable_notifications: bool = Field(True, description="Enable completion notifications")
quality_threshold: float = Field(0.7, description="Minimum quality score threshold")
class ProcessVideoResponse(BaseModel):
job_id: str
status: str
message: str
estimated_completion_time: Optional[float] = None
class PipelineStatusResponse(BaseModel):
job_id: str
status: str
progress_percentage: float
current_message: str
video_metadata: Optional[Dict[str, Any]] = None
result: Optional[Dict[str, Any]] = None
error: Optional[Dict[str, Any]] = None
processing_time_seconds: Optional[float] = None
@router.post("/process", response_model=ProcessVideoResponse)
async def process_video(
request: ProcessVideoRequest,
pipeline: SummaryPipeline = Depends(),
websocket_manager: WebSocketManager = Depends()
):
"""Process YouTube video through complete pipeline"""
try:
config = PipelineConfig(
summary_length=request.summary_length,
focus_areas=request.focus_areas or [],
include_timestamps=request.include_timestamps,
quality_threshold=request.quality_threshold,
enable_notifications=request.enable_notifications
)
# Create progress callback for WebSocket notifications
async def progress_callback(job_id: str, progress):
await websocket_manager.send_progress_update(job_id, {
"stage": progress.stage.value,
"percentage": progress.percentage,
"message": progress.message,
"details": progress.current_step_details
})
# Start pipeline processing
job_id = await pipeline.process_video(
video_url=str(request.video_url),
config=config,
progress_callback=progress_callback
)
return ProcessVideoResponse(
job_id=job_id,
status="processing",
message="Video processing started",
estimated_completion_time=120.0 # 2 minutes estimate
)
except Exception as e:
raise HTTPException(
status_code=500,
detail=f"Failed to start processing: {str(e)}"
)
@router.get("/process/{job_id}", response_model=PipelineStatusResponse)
async def get_pipeline_status(
job_id: str,
pipeline: SummaryPipeline = Depends()
):
"""Get pipeline processing status and results"""
result = await pipeline.get_pipeline_result(job_id)
if not result:
raise HTTPException(status_code=404, detail="Pipeline job not found")
# Calculate progress percentage based on stage
stage_percentages = {
PipelineStage.INITIALIZED: 0,
PipelineStage.VALIDATING_URL: 5,
PipelineStage.EXTRACTING_METADATA: 15,
PipelineStage.EXTRACTING_TRANSCRIPT: 35,
PipelineStage.ANALYZING_CONTENT: 50,
PipelineStage.GENERATING_SUMMARY: 75,
PipelineStage.VALIDATING_QUALITY: 90,
PipelineStage.COMPLETED: 100,
PipelineStage.FAILED: 0,
PipelineStage.CANCELLED: 0
}
response_data = {
"job_id": job_id,
"status": result.status.value,
"progress_percentage": stage_percentages.get(result.status, 0),
"current_message": f"Status: {result.status.value}",
"video_metadata": result.video_metadata,
"processing_time_seconds": result.processing_time_seconds
}
# Include results if completed
if result.status == PipelineStage.COMPLETED:
response_data["result"] = {
"summary": result.summary,
"key_points": result.key_points,
"main_themes": result.main_themes,
"actionable_insights": result.actionable_insights,
"confidence_score": result.confidence_score,
"quality_score": result.quality_score,
"cost_data": result.cost_data
}
# Include error if failed
if result.status == PipelineStage.FAILED and result.error:
response_data["error"] = result.error
return PipelineStatusResponse(**response_data)
@router.delete("/process/{job_id}")
async def cancel_pipeline(
job_id: str,
pipeline: SummaryPipeline = Depends()
):
"""Cancel running pipeline"""
success = await pipeline.cancel_pipeline(job_id)
if not success:
raise HTTPException(status_code=404, detail="Pipeline job not found or already completed")
return {"message": "Pipeline cancelled successfully"}
```
### File Locations and Structure
[Source: docs/architecture.md#project-structure]
**Backend Files**:
- `backend/services/summary_pipeline.py` - Main pipeline orchestration service
- `backend/api/pipeline.py` - Pipeline management endpoints
- `backend/core/websocket_manager.py` - WebSocket progress notifications
- `backend/models/pipeline.py` - Pipeline result storage models
- `backend/services/notification_service.py` - Completion notifications
- `backend/tests/unit/test_summary_pipeline.py` - Unit tests
- `backend/tests/integration/test_pipeline_api.py` - Integration tests
### Frontend Integration
[Source: docs/architecture.md#frontend-architecture]
```typescript
// frontend/src/hooks/usePipelineProcessor.ts
import { useState, useCallback } from 'react';
import { useMutation, useQuery } from '@tanstack/react-query';
import { apiClient } from '@/services/apiClient';
import { useWebSocket } from './useWebSocket';
interface PipelineConfig {
summary_length: 'brief' | 'standard' | 'detailed';
focus_areas?: string[];
include_timestamps: boolean;
enable_notifications: boolean;
quality_threshold: number;
}
interface PipelineProgress {
stage: string;
percentage: number;
message: string;
details?: any;
}
export function usePipelineProcessor() {
const [activeJobId, setActiveJobId] = useState<string | null>(null);
const [progress, setProgress] = useState<PipelineProgress | null>(null);
const { connect, disconnect } = useWebSocket({
onProgressUpdate: (update: PipelineProgress) => {
setProgress(update);
}
});
const startProcessing = useMutation({
mutationFn: async ({ url, config }: { url: string; config: PipelineConfig }) => {
const response = await apiClient.processVideo(url, config);
return response;
},
onSuccess: (data) => {
setActiveJobId(data.job_id);
connect(data.job_id);
}
});
const { data: pipelineStatus } = useQuery({
queryKey: ['pipeline-status', activeJobId],
queryFn: () => activeJobId ? apiClient.getPipelineStatus(activeJobId) : null,
enabled: !!activeJobId,
refetchInterval: (data) =>
data?.status === 'completed' || data?.status === 'failed' ? false : 2000
});
const cancelProcessing = useCallback(async () => {
if (activeJobId) {
await apiClient.cancelPipeline(activeJobId);
setActiveJobId(null);
setProgress(null);
disconnect();
}
}, [activeJobId, disconnect]);
return {
startProcessing: startProcessing.mutateAsync,
cancelProcessing,
isProcessing: startProcessing.isPending || (pipelineStatus?.status === 'processing'),
progress: progress || (pipelineStatus ? {
stage: pipelineStatus.status,
percentage: pipelineStatus.progress_percentage,
message: pipelineStatus.current_message
} : null),
result: pipelineStatus?.result,
error: startProcessing.error || pipelineStatus?.error,
pipelineStatus
};
}
```
### Quality Assurance Features
- **Automatic Retry Logic**: Failed or low-quality summaries automatically retried with improved parameters
- **Content-Aware Processing**: Different strategies for technical, educational, and entertainment content
- **Quality Scoring**: Multi-factor quality assessment ensures consistent results
- **Progress Transparency**: Detailed progress tracking keeps users informed throughout the process
- **Error Recovery**: Comprehensive error handling with graceful degradation
## Change Log
| Date | Version | Description | Author |
|------|---------|-------------|--------|
| 2025-01-25 | 1.0 | Initial story creation | Bob (Scrum Master) |
## Dev Agent Record
### Story Preparation - James (Full Stack Developer) - 2025-01-25
**Service Dependencies Created**: All required dependency services have been implemented to support the pipeline orchestration:
1. **CacheManager** (`backend/services/cache_manager.py`)
- ✅ In-memory cache implementation with TTL expiration
- ✅ Pipeline result caching and retrieval
- ✅ Transcript and video metadata caching
- ✅ Summary caching with configurable keys
- ✅ Automatic cleanup and cache statistics
- ✅ Redis-ready architecture for production scaling
2. **WebSocketManager** (`backend/core/websocket_manager.py`)
- ✅ Singleton pattern WebSocket connection management
- ✅ Job-specific connection tracking and messaging
- ✅ Progress update broadcasting to connected clients
- ✅ Completion and error notification system
- ✅ Heartbeat mechanism and stale connection cleanup
- ✅ Connection statistics and monitoring
3. **Pipeline Data Models** (`backend/models/pipeline.py`)
- ✅ PipelineStage enum with complete workflow stages
- ✅ PipelineConfig dataclass for processing configuration
- ✅ PipelineProgress tracking for real-time updates
- ✅ PipelineResult comprehensive result storage
- ✅ Pydantic models for API request/response validation
- ✅ Quality metrics and statistics models
4. **NotificationService** (`backend/services/notification_service.py`)
- ✅ Multi-type notification system (completion, error, progress, system)
- ✅ Notification history and statistics tracking
- ✅ Configurable notification filtering and management
- ✅ Email/webhook integration ready architecture
- ✅ Summary preview generation and formatting
**Anthropic Integration Alignment**: Updated story documentation to reference `AnthropicSummarizer` instead of `OpenAISummarizer` to align with implemented AI service integration.
**Story Status**: ✅ COMPLETED. Full end-to-end pipeline orchestration implemented with comprehensive service foundation.
### Implementation Completed - James (Full Stack Developer) - 2025-01-25
**Core Implementation**:
1. **✅ SummaryPipeline Service** (`backend/services/summary_pipeline.py`)
- Complete async pipeline orchestration with 7 processing stages
- Intelligent content analysis and configuration optimization
- Automatic retry logic with exponential backoff
- Quality validation and scoring system
- Real-time progress tracking via WebSocket
- Cache integration for performance optimization
- Comprehensive error handling and recovery
2. **✅ Pipeline API** (`backend/api/pipeline.py`)
- RESTful endpoints for pipeline management
- Process video endpoint with configuration support
- Status monitoring and job history endpoints
- Pipeline cancellation and cleanup functionality
- Health checks and system statistics
- Comprehensive error handling and validation
3. **✅ Testing Suite**
- Unit tests: `backend/tests/unit/test_summary_pipeline.py` (25+ test cases)
- Integration tests: `backend/tests/integration/test_pipeline_api.py` (20+ test scenarios)
- Edge case testing and error condition coverage
- Mock-based testing for isolated service validation
**Key Features Implemented**:
- **Intelligent Processing**: Content-aware optimization based on video metadata and transcript analysis
- **Quality Assurance**: Multi-factor quality scoring with automatic retry for poor results
- **Real-time Updates**: WebSocket progress notifications and completion alerts
- **Caching Strategy**: Multi-level caching for transcripts, metadata, and pipeline results
- **Error Recovery**: Comprehensive retry logic with improved configurations
- **Performance**: Async processing with background task management
- **Monitoring**: Detailed statistics, health checks, and job history tracking
**All Acceptance Criteria Met**:
1. ✅ Integrated pipeline connects URL validation → transcript extraction → AI summarization
2. ✅ Pipeline handles complete workflow asynchronously with progress tracking
3. ✅ System provides intelligent summary optimization based on transcript characteristics
4. ✅ Generated summaries include enhanced metadata (video info, processing stats, quality scores)
5. ✅ Pipeline includes quality validation and automatic retry for failed summaries
6. ✅ Users can monitor pipeline progress and receive completion notifications
## QA Results
*Results from QA Agent review of the completed story implementation will be added here*
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